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- Seipel, Stefan, Professor, 1968-, et al.
(författare)
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Visualisation of 3D Property Data and Assessment of the Impact of Rendering Attributes
- 2020
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Ingår i: Journal of Geovisualization and Spatial Analysis. - : Springer. - 2509-8810 .- 2509-8829. ; 4:2
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Tidskriftsartikel (refereegranskat)abstract
- Visualisations of 3D cadastral information incorporating both intrinsically spatial andnon-spatial information are examined here. The design of a visualisation prototype islinked to real case 3D property information. In an interview with domain experts, thefunctional and visual features of the prototype are assessed. The choice of renderingattributes was identified as an important aspect for further analysis. A computationalapproach to systematic assessment of the consequences of different graphical designchoices is proposed. This approach incorporates a colour similarity metric, visualsaliency maps, and k-nearest neighbour (kNN) classification to estimate risks ofconfusing or overlooking relevant elements in a visualisation.The results indicate that transparency is not an independent visual variable, as itaffects the apparent colour of 3D objects and makes them inherently more difficult todistinguish. Transparency also influences visual saliency of objects in a scene. Theproposed analytic approach was useful for visualisation design and revealed that theconscious use of graphical attributes, like combinations of colour, transparency andline styles, can improve saliency of objects in a 3D scene.
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- Aslani, Mohammad, et al.
(författare)
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A fast instance selection method for support vector machines in building extraction
- 2020
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Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 97
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Tidskriftsartikel (refereegranskat)abstract
- Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.
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- Aslani, Mohammad, et al.
(författare)
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A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data
- 2022
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Ingår i: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM. - Setúbal : ScitePress. - 9789897585715 ; , s. 56-63
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Konferensbidrag (refereegranskat)abstract
- Rooftop solar energy has long been regarded as a promising solution to cities’ growing energy demand and environmental problems. A reliable estimate of rooftop solar energy facilitates the deployment of photovoltaics and helps formulate renewable-related policies. This reliable estimate underpins the necessity of accurately pinpointing the areas utilizable for mounting photovoltaics. The size, shape, and superstructures of rooftops as well as shadow effects are the important factors that have a considerable impact on utilizable areas. In this study, the utilizable areas and solar energy potential of rooftops are estimated by considering the mentioned factors using a three-step methodology. The first step involves training PointNet++, a deep network for object detection in point clouds, to recognize rooftops in LiDAR data. Second, planar segments of rooftops are extracted using clustering. Finally, areas that receive sufficient solar irradiation, have an appropriate size, and fulfill photovoltaic installation requirements are identified using morphological operations and predefined thresholds. The obtained results show high accuracy for rooftop extraction (93%) and plane segmentation (99%). Moreover, the spatially detailed analysis indicates that 17% of rooftop areas are usable for photovoltaics.
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- Aslani, Mohammad, et al.
(författare)
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Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment
- 2022
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 306
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Tidskriftsartikel (refereegranskat)abstract
- The considerable potential of rooftop photovoltaics (RPVs) for alleviating the high energy demand of cities has made them a proven technology in local energy networks. Identification of rooftop areas suitable for installing RPVs is of importance for energy planning. Having these suitable areas referred to as utilizable areas greatly assists in a reliable estimate of RPVs energy production. Within such a context, this research aims to propose a spatially detailed methodology that involves (a) automatic extraction of buildings footprint, (b) automatic segmentation of roof faces, and (c) automatic identification of utilizable areas of roof faces for solar infrastructure installation. Specifically, the innovations of this work are a new method for roof face segmentation and a new method for the identification of utilizable rooftop areas. The proposed methodology only requires digital surface models (DSMs) as input, and it is independent of other auxiliary spatial data to become more functional. A part of downtown Gothenburg composed of vegetation and high-rise buildings with complex shapes was selected to demonstrate the methodology performance. According to the experimental results, the proposed methodology has a high success rate in building extraction (about 95% correctness and completeness) and roof face segmentation (about 85% completeness and correctness). Additionally, the results suggest that the effects of roof occlusions and roof superstructures are satisfactorily considered in the identification of utilizable rooftop areas. Thus, the methodology is practically effective and relevant for the detailed RPVs assessments in arbitrary urban regions where only DSMs are accessible.
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- Aslani, Mohammad, et al.
(författare)
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Continuous residual reinforcement learning for traffic signal control optimization
- 2018
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Ingår i: Canadian journal of civil engineering (Print). - : NRC Research Press. - 0315-1468 .- 1208-6029. ; 45:8, s. 690-702
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Tidskriftsartikel (refereegranskat)abstract
- Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on continuous reinforcement learning. Although they have been successful in traffic signal control, they may become unstable and fail to converge to near-optimal solutions. We develop adaptive traffic signal controllers based on continuous residual reinforcement learning (CRL-TSC) that is more stable. The effect of three feature functions is empirically investigated in a microscopic traffic simulation. Furthermore, the effects of departing streets, more actions, and the use of the spatial distribution of the vehicles on the performance of CRL-TSCs are assessed. The results show that the best setup of the CRL-TSC leads to saving average travel time by 15% in comparison to an optimized fixed-time controller.
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- Aslani, Mohammad, et al.
(författare)
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Developing adaptive traffic signal control by actor-critic and direct exploration methods
- 2019
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Ingår i: Proceedings of the Institution of Civil Engineers. - : Thomas Telford. - 0965-092X .- 1751-7710. ; 172:5, s. 289-298
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Tidskriftsartikel (refereegranskat)abstract
- Designing efficient traffic signal controllers has always been an important concern in traffic engineering. This is owing to the complex and uncertain nature of traffic environments. Within such a context, reinforcement learning has been one of the most successful methods owing to its adaptability and its online learning ability. Reinforcement learning provides traffic signals with the ability automatically to determine the ideal behaviour for achieving their objective (alleviating traffic congestion). In fact, traffic signals based on reinforcement learning are able to learn and react flexibly to different traffic situations without the need of a predefined model of the environment. In this research, the actor-critic method is used for adaptive traffic signal control (ATSC-AC). Actor-critic has the advantages of both actor-only and critic-only methods. One of the most important issues in reinforcement learning is the trade-off between exploration of the traffic environment and exploitation of the knowledge already obtained. In order to tackle this challenge, two direct exploration methods are adapted to traffic signal control and compared with two indirect exploration methods. The results reveal that ATSC-ACs based on direct exploration methods have the best performance and they consistently outperform a fixed-time controller, reducing average travel time by 21%.
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